• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.522-539
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• 2021

In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

• 한국산업정보학회논문지
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• 제18권4호
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• pp.25-35
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• 2013

본 논문에서는 개인화된 분위기 분류 모델 대신에 대중의 분위기 분류 모델을 제안한다. 분위기 판별 성능을 개선하기 위해 두 가지 접근 방법을 선택하였는데, 그 첫 번째가 표준편차에 기초한 특징축소이다. 이는 음악의 특징을 추출하기 위해 사용하는 MIRtoolbox에서 추출되는 391개의 특징들을 모두 사용할 경우의 성능 저하 문제를 해결하기 위한 방법이다. 실험결과, 본 논문에서 제안한 특징축소 방법이 기존의 차원 축소 방법인 R-Square와 PCA보다 성능이 좋음을 확인할 수 있었다. 그리고 특징축소 방법만으로는 성능 개선에 한계가 있어 두 번째 개선방법으로 단위 신경망을 사용하여 추가의 성능 개선을 시도하였다. 실험결과 이 역시 유효한 성능 개선이 이루어짐을 확인할 수 있었다.

• 정보처리학회논문지B
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• 제15B권5호
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• pp.435-448
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• 2008

사용자 포즈의 3차원 데이터 생성을 통한 3차원 포즈 인식은 2차원 포즈 인식의 문제점을 해결하기 위해서 많이 연구되고 있지만, 3차원 표면 데이터의 방대한 양으로 포즈 인식에서 중요한 특징 추출(feature extraction)이 어렵고 수행 시간이 많이 걸리는 문제점을 가지고 있다. 본 논문에서는 3차원 포즈 인식의 두 가지 문제점인 특징 추출의 어려움과 느린 처리속도를 개선하기 위해서 3차원 형상복원 기술로 모델의 3차원 표면 점들로 구성된 데이터를 2차원 데이터로 변환하는 차원 축소(dimension reduction) 방법을 제안한다. 실린더형 외곽점을 이용한 메쉬없는 매개변수화(meshless parameterization) 방법은 방대한 데이터인 3차원 포즈 데이터를 2차원 데이터로 변환하여 특징 추출과 매칭과정의 연산 속도를 향상 시키며, 특징 추출의 효율성 검증을 위해 간단한 환경에서 실험이 가능한 손 포즈 인식 및 인간 포즈 인식에 적용하였다.

• 융합신호처리학회논문지
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• 제7권3호
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• pp.116-121
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• 2006

본 논문에서는 다양한 잡음환경에서의 실시간 적응화 기법을 적용하기 위한 선결 과제로 다차원 음성 특정 벡터를 저차원으로 축소하는 방법을 제안한다. 제안된 방법은 특징 벡터를 확률 우도 값으로 매핑시켜 비선형적으로 축소하는 방법으로 음성 / 비음성의 분류는 우도비 검증 (Likelihood Ratio Test; LRT) 을 이용하여 분류하였다. 실험 결과 고차원 특징 벡터를 이용하여 분류한 결과와 대등하게 분류됨을 확인할 수 있었다. 그리고, 제안된 방법에 의해 검출된 음성 데이터를 이용한 음성인식 실험에서도 10차 MFCC(Mel-Frequency Cepstral Coefficient)를 사용하여 분류한 경우와 대등한 인식률을 보여주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.7-11
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• 2010

In this paper, we propose an improved speech/music discrimination method based on a feature combination and dimensionality reduction approach. To improve discrimination ability, we use a feature based on spectral duration analysis and employ the hierarchical dimensionality reduction (HDR) method to reduce the effect of correlated features. Through various kinds of experiments on speech and music, it is shown that the proposed method showed high discrimination results when compared with conventional methods.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.74-88
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• 1995

This article introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived on the impurity reduction (IR) measure of divergence, which is proposed to extend the proportional-reduction-in-error (PRE) measure in the decision-theory context. For the method derivation, the IR measure is analyzed to characterize its statistical properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. A numerical example is considered to illustrate the proposed approach.

• Journal of Information Processing Systems
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• 제14권5호
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

• 한국음향학회지
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• 제36권2호
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• pp.144-150
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• 2017

음악 장르는 음악 검색 및 분류 등의 정보 처리 시스템 구현에 있어서 필수적인 요소이다. 일반적으로 장르 분류를 위한 스펙트럼 특징은 음악의 화음 및 강약 구조를 표현하기 위해 부밴드로 분해하여 구해진다. 본 논문은 음악 장르 분류 성능 개선을 위한 특징 추출을 위한 부밴드 분해 방법에 관해 연구하였다. 또한 부밴드 음악 특징의 차수를 줄일 수 있는 방법에 대해서도 연구하였다. 널리 사용되고 있는 장르 데이터셋들에서 실험을 수행하여 널리 사용되고 있는 옥타브 스케일보다 세분화된 부밴드 분해가 장르 분류 성능을 향상시킬 수 있으며, 특징 차수 축소를 결합하여 분류기의 계산량도 줄일 수 있음을 보였다.